Best Practices for Using Claude Code

Recently, I adopted a coding tip from the Anthropic team that has significantly boosted the quality of my AI-generated code. Anthropic runs multiple Claude instances in parallel to dramatically improve code quality compared to single-instance workflows. How it works: (1) One Claude writes the code, the coder - focusing purely on implementation (2) A second Claude reviews it, the reviewer - examining with fresh context, free from implementation bias (3) A third Claude applies fixes, the fixer - integrating feedback without defensiveness This technique works with any AI assistant, not just Claude. Spin each agent up in its own tab—Cursor, Windsurf, or plain CLI. Then, let Git commits serve as the hand-off protocol. This separation mimics human pair programming but supercharges it with AI speed. When a single AI handles everything, blind spots emerge naturally. Multiple instances create a system of checks and balances that catch what monolithic workflows miss. This shows that context separation matters. By giving each AI a distinct role with clean context boundaries, you essentially create specialized AI engineers, each bringing a unique perspective to the problem. This and a dozen more tips for developers building with AI in my latest AI Tidbits post https://lnkd.in/gTydCV9b

Coding with AI isn't just about speed anymore. It's about strategy. And Claude Code (and OpenAI’s Codex) might be the first agent that actually thinks like a teammate. Not a chatbot that happens to write code. But a programmable co-worker with real autonomy. Here's how the engineers at Anthropic actually use it: They write README-style memory just for Claude → A file called CLAUDE.md sits in your repo and teaches the AI how to work with your stack, your tools, and your team's quirks. They set up slash commands for reusable workflows → Think: /fix-linter-warnings or /triage-open-issues. These are markdown prompt templates you drop into .claude/commands and reuse across sessions. They use Claude like a project lead, not an intern → The best engineers don’t ask Claude to just "write code." They: Ask it to read and understand files Prompt it to "think hard" or "ultrathink" before building Then ask it to write a plan before shipping code They automate onboarding → New hires just start talking to Claude. Instead of asking a team lead, they ask: "How does logging work here?" "Why are we using X over Y on line 134?" "How do I add a new API route?" They run multi-agent workflows → One Claude writes code. Another reviews it. A third patches it. Each runs in a separate terminal or worktree. They even automate Claude itself → Headless mode lets you run Claude programmatically inside CI pipelines, git hooks, or across massive code migrations. — Agentic coding isn’t just about making an AI write functions. It's about making it collaborate across your entire stack. (👉 Credit to Anthropic's engineering blog for this breakdown) — Enjoyed this? 2 quick things: - Follow me for more AI automation insights - Share this a with teammate 

I've been all-in on Claude Code as my pair programmer lately, and it's genuinely changed how I approach development projects. Unlike other AI coding tools that just spit out random snippets, Claude Code actually understands your entire project context. It works like having an experienced developer who respects your existing workflows and can execute complex tasks from start to finish. Here's what makes it stand out for me: → It's sandboxed to your project folder, so it truly understands your codebase → Planning mode lets you iterate on the approach before writing any code → It follows your coding guidelines from a simple Claude.md file → Terminal-based interface feels natural for developers → Integrates with your existing tools (don't replace start.spring.io - use them together!) My workflow: Start with planning mode, break tasks into small chunks, use branches to protect your codebase, and let it handle the implementation details while you focus on architecture decisions. In my latest video, I show building a complete Spring Boot REST API with caching - from planning to testing. The tool even runs integration tests and validates the endpoints automatically. Check out the full demo: https://lnkd.in/e4iZrf8Q What coding tools are you using in your daily workflow? Have you tried any agentic coding assistants? #SoftwareDevelopment #AI #DevTools #ClaudeCode #SpringBoot

Claude doesn’t make your code better. Your process does. Some teams are getting huge mileage from AI coding assistants. But it’s not because the models are magical. It’s because their process is. One example comes from the team at Julep. They ship real production code with Claude’s help, but not by treating it like an all-knowing pair. Instead, they treat it like a sharp but clueless intern: fast, tireless, and completely unaware of context. To make that work, they built process around it. A `CLAUDE.md` file lays down project rules and architecture decisions. Anchor comments in the code give Claude inline guardrails. Git worktrees isolate experiments. Commit tags disclose which code was AI-assisted. And tests? Always written by a human. No exceptions. Over time, they found three reliable modes of working with Claude: 📌First-drafter (for boilerplate), 📌Pair-programmer (for shaping real features), 📌Validator (for reviewing edge cases). Each one useful, but only with the right boundaries. The takeaway here isn’t about Claude. It’s about the delta between teams that just use AI and teams that integrate it. The second kind ship faster, break less, and stay sane. Not because they’re better engineers, just because they have better habits. Follow Pratik Daga for more posts on software engineering.